diff --git a/CHANGELOG.md b/CHANGELOG.md
index 0360767f..b227507f 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -2,6 +2,16 @@
 # Changelog
 All notable changes to this project will be documented in this file.
 
+### [2.0.4]
+
+#### Fixed 
+
+- Fix segmentation num_data_train sorting
+
+#### Added
+
+- Add default presorting to segmentation samples
+
 ### [2.0.3]
 
 #### Fixed 
diff --git a/pyproject.toml b/pyproject.toml
index 47c7ecd7..be02f35d 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -1,6 +1,6 @@
 [tool.poetry]
 name = "quadra"
-version = "2.0.3"
+version = "2.0.4"
 description = "Deep Learning experiment orchestration library"
 authors = [
   "Federico Belotti <federico.belotti@orobix.com>",
diff --git a/quadra/__init__.py b/quadra/__init__.py
index 0525ae75..6ef6310e 100644
--- a/quadra/__init__.py
+++ b/quadra/__init__.py
@@ -1,4 +1,4 @@
-__version__ = "2.0.3"
+__version__ = "2.0.4"
 
 
 def get_version():
diff --git a/quadra/datamodules/segmentation.py b/quadra/datamodules/segmentation.py
index 5fc5058a..a9bedc1c 100644
--- a/quadra/datamodules/segmentation.py
+++ b/quadra/datamodules/segmentation.py
@@ -590,14 +590,34 @@ def _prepare_data(self) -> None:
             masks_train = samples_and_masks_train[:, 0, 1]
             masks_val = samples_and_masks_val[:, 0, 1]
 
+        # Pre-ordering train and val samples for determinism
+        # They will be shuffled (with a seed) during training
+        sorting_indices_train = np.argsort(list(samples_train))
+        samples_train = [samples_train[i] for i in sorting_indices_train]
+        targets_train = [targets_train[i] for i in sorting_indices_train]
+        masks_train = [masks_train[i] for i in sorting_indices_train]
+
+        sorting_indices_val = np.argsort(samples_val)
+        samples_val = [samples_val[i] for i in sorting_indices_val]
+        targets_val = [targets_val[i] for i in sorting_indices_val]
+        masks_val = [masks_val[i] for i in sorting_indices_val]
+
         if self.exclude_good:
             samples_train = list(np.array(samples_train)[np.array(targets_train)[:, 0] == 0])
             masks_train = list(np.array(masks_train)[np.array(targets_train)[:, 0] == 0])
             targets_train = list(np.array(targets_train)[np.array(targets_train)[:, 0] == 0])
 
         if self.num_data_train is not None:
+            # Generate a random permutation
+            random_permutation = list(range(len(samples_train)))
             random.seed(self.seed)
-            random.shuffle(samples_train)
+            random.shuffle(random_permutation)
+
+            # Shuffle samples_train, targets_train, and masks_train using the same permutation
+            samples_train = [samples_train[i] for i in random_permutation]
+            targets_train = [targets_train[i] for i in random_permutation]
+            masks_train = [masks_train[i] for i in random_permutation]
+
             samples_train = np.array(samples_train)[: self.num_data_train]
             targets_train = np.array(targets_train)[: self.num_data_train]
             masks_train = np.array(masks_train)[: self.num_data_train]